Communication method and related equipment

Abstract

The invention discloses a communication method and related equipment, which are used for enabling the computing power of a communication node to be applied to artificial intelligence (AI) processing of a neural network, and also can improve the deployment flexibility of the neural network. In the method, first data received by a first communication device is data after first processing, and second data sent by the first communication device is gradient data obtained based on data after second processing of the first data and label data; the second data is the data after the first processing, and the first data is gradient data obtained based on the data after the second processing of the second data and the label data. And the first processing comprises AI processing, and / or the second processing comprises AI processing. In other words, the second data is gradient data corresponding to data obtained through AI processing based on the first data, or the first data is gradient data corresponding to data obtained through AI processing based on the second data.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a communication method and related equipment. Background Art

[0002] Wireless communication can be the transmission communication between two or more communication nodes without propagation through conductors or cables. The communication nodes generally include network equipment and terminal equipment.

[0003] At present, in wireless communication systems, communication nodes generally have signal transceiving capabilities and computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computing power support for signal transceiving capabilities (for example: sending and receiving signals) to achieve communication between network devices and other communication nodes.

[0004] However, in a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a communication method and related equipment, which are used to enable the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment.

[0006] The first aspect of the present application provides a communication method, which is performed by a first communication device, which may be a communication device (such as a terminal device), or the first communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the first communication device receives configuration information, which is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the first communication device receives the first data based on the first information; the first communication device sends the second information, and the second information is used to schedule the transmission of the second data; the first communication device sends the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0007] Based on the above technical solution, the first data is data after the first processing and the second data is gradient data obtained based on the first data after the second processing and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data. In addition, the first processing includes AI processing, and / or the second processing includes AI processing. In other words, the second data is the gradient data corresponding to the data obtained by performing AI processing on the first data, or the first data is the gradient data corresponding to the data obtained by performing AI processing on the second data. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to AI processing while improving the flexibility of AI deployment.

[0008] In addition, the configuration information received by the first communication device is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data. Accordingly, the first communication device can receive the first data based on the first information; and after the first communication device sends the second information for scheduling the transmission of the second data, the first communication device can send the second data based on the second information. In other words, in the case where the first data and / or the second data are data associated with AI processing, the first communication device can realize the transmission of data associated with AI processing based on the scheduling of the first information and the second information. Thus, by scheduling the data associated with AI processing through the first information and the second information, the transmission success rate of the data associated with AI processing can be improved.

[0009] In the present application, since the first processing includes AI processing, and / or the second processing includes AI processing; therefore, based on the data after the first processing or the second processing and the label data, gradient data and / or the result of the loss function can be obtained. Accordingly, in the present application, the gradient data can be replaced by the result of the loss function, the gradient data and the result of the loss function, etc.

[0010] In this application, the terms AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0011] In the present application, the data involved (such as first data, second data, etc.) can be replaced by information, signals, etc.

[0012] It should be understood that the second data may be data obtained based on the first data. For example, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, the first data is data sent by the sender of the first data (e.g., the second communication device) after the first processing, and the second data is data obtained by the first communication device based on the received first data after the second processing. In this case, the first data may be referred to as forward data, and the second data may be referred to as reverse data (e.g., reverse gradient, result of loss function, etc.).

[0013] It should be understood that the first data may be data obtained based on the second data. For example, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data, the second data is data sent by the first communication device after the first processing, and the first data is data obtained by the sender of the first data (e.g., the second communication device) performing the second processing based on the received second data. In this case, the second data may be referred to as forward data, and the first data may be referred to as reverse data (e.g., reverse gradient, result of loss function, etc.).

[0014] Optionally, in the above process, if the first processing includes AI processing, the AI ​​processing in the first processing can be called encoding neural network processing, AI encoder processing, AI encoding neural network processing, etc. Correspondingly, if the second processing includes AI processing, the AI ​​processing in the second processing can be called decoding neural network processing, AI decoder processing, AI decoding neural network processing, etc.

[0015] It should be noted that, in a wireless communication system, the first information used to schedule the first data and / or the second information used to schedule the second data can be messages / signaling / information of a radio resource control (RRC) layer, a medium access control (MAC) layer, a physical (PHY) layer or other protocol layers.

[0016] In addition, compared to the implementation method in which the communication device processes the received application layer data through the physical layer and then dequantizes the physical layer processing results to obtain the application layer scheduling signaling (the scheduling signaling is used to schedule the transmission of data associated with AI processing), when the first information used to schedule the first data and / or the second information used to schedule the second data is physical layer signaling, the transmission of data associated with AI processing can be quickly scheduled through physical layer signaling, thereby reducing the processing delay.

[0017] Exemplarily, take the example that the first information for scheduling the first data comes from the second communication device. The second communication device may be a network device, and accordingly, the first information may be downlink control information (DCI) sent by the network device to the terminal device, and the second information may be uplink control information (UCI) sent by the terminal device to the network device. Alternatively, the second communication device may be another terminal device different from the first communication device, and accordingly, the first information and the second information may be sidelink control information (SCI) exchanged between different terminal devices.

[0018] In a possible implementation manner of the first aspect, the transmission resource of the first information includes a time domain resource carrying the first information; wherein a time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

[0019] Based on the above technical solution, the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured. In this way, after receiving the first information, the first communication device can determine the time domain resource carrying the second information based on the preconfigured time interval and the time domain resource carrying the first information, and send the second information on the time domain resource carrying the second information. In addition, the second communication device can also receive the second information based on the preconfigured time domain resource, which can reduce the resource configuration overhead of the second information.

[0020] In a possible implementation manner of the first aspect, the first data is data after a first processing and the second data is gradient data obtained based on the first data after a second processing and label data; the first processing satisfies any of the following: the first processing is triggered based on the second information, and the first information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

[0021] In a possible implementation manner of the first aspect, the second data is data after first processing and the first data is gradient data obtained based on the second data after second processing and label data; the first processing satisfies any of the following: the first processing is triggered based on the first information, and the second information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

[0022] Based on the above technical solution, since the first processing includes AI processing, and / or the second processing includes AI processing, for this reason, the above implementation method can trigger the scheduling of AI data through AI processing, or the above implementation method can trigger AI processing through the scheduling of AI data. Thus, the AI ​​processing and the scheduling of AI data can trigger each other, thereby reducing the interaction of the trigger indication of AI processing or the trigger indication of the scheduling of AI data, which can reduce the processing delay and reduce the overhead. Alternatively, the above implementation method can trigger AI processing through the transmission of AI data. Thus, the AI ​​processing and the transmission of AI data can trigger each other, thereby reducing the interaction of the trigger indication of AI processing, which can reduce the processing delay and reduce the overhead.

[0023] In a possible implementation of the first aspect, the configuration information includes a first configuration and a second configuration, the first configuration is used to configure the search space of the first information, and the second configuration is used to configure the retransmission timer of the second information; wherein the time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

[0024] Based on the above technical solution, the configuration information for configuring the transmission resource of the first information may include a first configuration and a second configuration, the first configuration is used to configure the search space of the first information, and the second configuration is used to configure the retransmission timer of the second information. In this way, the first communication device can receive the first information and retransmit the second information based on the configuration information, thereby improving the transmission success rate of the first information and the second information.

[0025] In addition, since the first information may be mutually triggered with the AI ​​processing, that is, the first information may be executed irregularly. For this reason, by implementing the time length of the period corresponding to the search space less than or equal to the time length of the retransmission timer, the first communication device can detect the first information in a period of shorter time length, so that the first communication device can timely receive the data after AI processing or timely trigger the AI ​​processing. Moreover, the timer with a longer time length can reduce the overhead of the first communication device retransmitting the second information.

[0026] In a possible implementation manner of the first aspect, the method further includes: the first communication device sends first indication information, where the first indication information is used to indicate whether the first information is received correctly.

[0027] In this application, whether it is correctly received can be replaced by other terms, including but not limited to: whether it is incorrectly received, whether it is correctly parsed, whether it is incorrectly parsed, etc.

[0028] Based on the above technical solution, the first communication device can also send a first indication information, so that the second communication device can clarify whether the first communication device correctly receives the first information based on the first indication information, and subsequently the second communication device can determine whether to retransmit the first information and / or the first data scheduled by the first information based on the first indication information.

[0029] In addition, the first information is used to schedule the first data. Generally, the receiver of the first information and the first data feeds back whether the first data is correctly received, and the receiver does not feed back whether the first information is correctly received. In the above technical solution, if the first information can be used to trigger AI processing (such as the first processing and / or the second processing), for this purpose, the receiver of the first indication information can clearly know whether the first communication device triggers the corresponding AI processing based on the first information by indicating whether the first information is correctly received through the first indication information.

[0030] In a possible implementation manner of the first aspect, the first indication information indicates that the first information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and label data of the first data, the first indication information is also used to trigger the first processing; the first indication information indicates that the first information is correctly received, and when the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data of the second data, the first indication information is also used to trigger the second processing.

[0031] Based on the above technical solution, when the first indication information is used to indicate the correct reception of the first information, the first indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating the correct reception of the first information through the first indication information, the recipient of the first indication information can trigger the corresponding AI processing based on the first indication information.

[0032] In a possible implementation manner of the first aspect, the first indication information is further used to indicate whether to perform processing based on the first data.

[0033] Optionally, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first data may be data obtained based on the second data. To this end, the first indication information may also indicate whether to process based on the first data, which can be understood as indicating whether the first communication device further processes the first data based on the data after the second processing and label data to obtain gradient data.

[0034] Optionally, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data, the second data may be data obtained based on the first data. To this end, the first indication information may also indicate whether to perform processing based on the first data, which can be understood as indicating whether the first communication device performs gradient update processing based on the first data (i.e., gradient data).

[0035] Based on the above technical solution, the first indication information is used not only to indicate whether the first information is correctly received, but also to indicate whether to process based on the first data. In this way, the first indication information can be reused to implement more indications to reduce overhead.

[0036] In a possible implementation manner of the first aspect, after the first communication device sends the second information, the method further includes: the first communication device receives second indication information, where the second indication information is used to indicate whether the second information is received correctly.

[0037] Based on the above technical solution, after the first communication device sends the second information, the first communication device can also receive second indication information, so that the first communication device can determine whether the second communication device correctly receives the second information based on the second indication information, and subsequently the first communication device can determine whether to retransmit the second information and / or the second data scheduled by the second information based on the first indication information.

[0038] In a possible implementation manner of the first aspect, the second indication information indicates that the second information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and label data of the first data, the second indication information is also used to trigger the second processing; the second indication information indicates that the second information is correctly received, and when the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data of the second data, the second indication information is used to trigger the first processing.

[0039] Based on the above technical solution, when the second indication information is used to indicate the correct reception of the second information, the second indication information can also be used to trigger AI processing (for example, the first processing and / or the second processing). Thus, by indicating the correct reception of the second information through the second indication information, the recipient of the second indication information can trigger the corresponding AI processing based on the second indication information.

[0040] In a possible implementation manner of the first aspect, the second indication information is further used to indicate whether to perform processing based on the second data.

[0041] Optionally, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, the first data may be data obtained based on the second data. To this end, the second indication information may also indicate whether to perform processing based on the second data, which can be understood as indicating whether the first indication information may also indicate whether the second communication device performs gradient update processing based on the second data (i.e., gradient data).

[0042] Optionally, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second data may be data obtained based on the first data. To this end, the second indication information may also indicate whether to perform processing based on the second data, which can be understood as indicating whether the second communication device further processes the second data after the second processing and label data to obtain gradient data.

[0043] Based on the above technical solution, the second indication information is used not only to indicate whether the second information is correctly received, but also to indicate whether to process based on the second data. In this way, the second indication information can be reused to implement more indications to reduce overhead.

[0044] In a possible implementation manner of the first aspect, the second information is further used to indicate at least one of the following: a data type of the second data, and whether to send gradient information determined based on the second data.

[0045] Based on the above technical solution, the second information is also used to indicate at least one of the above items, so that the recipient of the second information (i.e., the second communication device) can obtain other information associated with the second data based on the second information, and assist in subsequent processing of the second data based on the other information.

[0046] In a possible implementation manner of the first aspect, the first information is further used to indicate at least one of the following: a data type of the first data, and whether to send gradient information determined based on the first data.

[0047] Based on the above technical solution, the first information is also used to indicate at least one of the above items, so that the recipient of the first information (i.e., the first communication device) can obtain other information associated with the first data based on the first information, and assist in subsequent processing of the first data based on the other information.

[0048] In a possible implementation manner of the first aspect, after the first communication device receives the first information, the method further includes: in a case where a parsing error occurs in the first information, the first communication device determines not to receive the first data.

[0049] Optionally, after the first communication device receives the first information, the method further includes: in case of a parsing error of the first information, the first communication device does not expect to receive the first data.

[0050] Based on the above technical solution, in the case where the first information is parsed incorrectly, the first communication device can determine not to receive the first data to avoid receiving erroneous data.

[0051] In a possible implementation manner of the first aspect, the method also includes: the first communication device sends capability information of the first communication device, and the capability information of the first communication device is used to determine the configuration information; wherein the AI ​​capability information of the first communication device includes at least one of the following: processing delay information of the first communication device for forward data of the AI ​​network structure to which the first data belongs, processing delay information of the first communication device for reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, load information of the processing resources of the first communication device, and computing power resource information of the first communication device.

[0052] Based on the above technical solution, the first communication device can send the capability information of the first communication device, so that the second communication device determines the configuration information adapted to the capability information based on the capability information, so that the first communication device can receive the first information based on the success rate of the configuration information.

[0053] Optionally, the configuration information includes at least one of the following: a period of the first information, a window length for detecting the first information, and a position of a transmission symbol of the first information in a time slot. Exemplarily, the configuration information may include a first configuration, and the at least one item may be included in the first configuration, and the first configuration is used to configure a search space for the first information.

[0054] In a possible implementation manner of the first aspect, the method also includes: the first communication device receives third information, and the third information is used to indicate at least one of the following: information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

[0055] Based on the above technical solution, the first communication device may also receive third information indicating at least one of the above items, so that the first communication device can perform subsequent AI processing based on the third information.

[0056] The second aspect of the present application provides a communication method, which is performed by a second communication device, which may be a communication device (such as a terminal device or a network device), or the second communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the second communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the second communication device sends configuration information, which is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the second communication device sends the first data based on the first information; the second communication device receives the second information, and the second information is used to schedule the transmission of the second data; the second communication device receives the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0057] Based on the above technical solution, the first data is data after the first processing and the second data is gradient data obtained based on the first data after the second processing and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data. In addition, the first processing includes AI processing, and / or the second processing includes AI processing. In other words, the second data is the gradient data corresponding to the data obtained by performing AI processing on the first data, or the first data is the gradient data corresponding to the data obtained by performing AI processing on the second data. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to AI processing while improving the flexibility of AI deployment.

[0058] In addition, the configuration information sent by the second communication device is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data. Accordingly, the second communication device can send the first data based on the first information; and after the second communication device receives the second information for scheduling the transmission of the second data, the second communication device can receive the second data based on the second information. In other words, in the case where the first data and / or the second data are data associated with AI processing, the second communication device can realize the transmission of data associated with AI processing based on the scheduling of the first information and the second information. Thus, by scheduling the data associated with AI processing through the first information and the second information, the transmission success rate of the data associated with AI processing can be improved.

[0059] In a possible implementation manner of the second aspect, the transmission resource of the first information includes a time domain resource carrying the first information; wherein the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

[0060] Based on the above technical solution, the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured. In this way, after the second communication device sends the first information, the first communication device can determine the time domain resource carrying the second information based on the preconfigured time interval and the time domain resource carrying the first information, and send the second information on the time domain resource carrying the second information. In addition, the second communication device can also receive the second information based on the preconfigured time domain resource, which can reduce the resource configuration overhead of the second information.

[0061] In a possible implementation manner of the second aspect, the first data is data after a first processing and the second data is gradient data obtained based on the first data after the second processing and label data; the first processing satisfies any of the following: the first processing is triggered based on the second information, and the first information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

[0062] In a possible implementation manner of the second aspect, the second data is data after first processing and the first data is gradient data obtained based on the second data after second processing and label data; the first processing satisfies any of the following: the first processing is triggered based on the first information, and the second information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

[0063] Based on the above technical solution, since the first processing includes AI processing, and / or the second processing includes AI processing, for this reason, the above implementation method can trigger the scheduling of AI data through AI processing, or the above implementation method can trigger AI processing through the scheduling of AI data. Thus, the AI ​​processing and the scheduling of AI data can trigger each other, thereby reducing the interaction of the trigger indication of AI processing or the trigger indication of the scheduling of AI data, which can reduce the processing delay and reduce the overhead. Alternatively, the above implementation method can trigger AI processing through the transmission of AI data. Thus, the AI ​​processing and the transmission of AI data can trigger each other, thereby reducing the interaction of the trigger indication of AI processing, which can reduce the processing delay and reduce the overhead.

[0064] In a possible implementation of the second aspect, the configuration information includes a first configuration and a second configuration, the first configuration is used to configure the search space of the first information, and the second configuration is used to configure the retransmission timer of the second information; wherein the time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

[0065] Based on the above technical solution, the configuration information for configuring the transmission resource of the first information may include a first configuration and a second configuration, the first configuration is used to configure the search space of the first information, and the second configuration is used to configure the retransmission timer of the second information. In this way, the first communication device can receive the first information and retransmit the second information based on the configuration information, thereby improving the transmission success rate of the first information and the second information.

[0066] In addition, since the first information may be mutually triggered with the AI ​​processing, that is, the first information may be executed irregularly. For this reason, by implementing the time length of the period corresponding to the search space less than or equal to the time length of the retransmission timer, the first communication device can detect the first information in a period of shorter time length, so that the first communication device can timely receive the data after AI processing or timely trigger the AI ​​processing. Moreover, the timer with a longer time length can reduce the overhead of the first communication device retransmitting the second information.

[0067] In a possible implementation manner of the second aspect, the method further includes: the second communication device receives first indication information, where the first indication information is used to indicate whether the first information is correctly received.

[0068] Based on the above technical solution, the second communication device can also receive the first indication information, so that the second communication device can clarify whether the first communication device correctly receives the first information based on the first indication information, and subsequently the second communication device can determine whether to retransmit the first information and / or the first data scheduled by the first information based on the first indication information.

[0069] In addition, the first information is used to schedule the first data. Generally, the receiver of the first information and the first data feeds back whether the first data is correctly received, and the receiver does not feed back whether the first information is correctly received. In the above technical solution, if the first information can be used to trigger AI processing (such as the first processing and / or the second processing), for this purpose, the receiver of the first indication information can clearly know whether the first communication device triggers the corresponding AI processing based on the first information by indicating whether the first information is correctly received through the first indication information.

[0070] In a possible implementation manner of the second aspect, the first indication information indicates that the first information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and label data of the first data, the first indication information is also used to trigger the first processing; the first indication information indicates that the first information is correctly received, and when the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data of the second data, the first indication information is also used to trigger the second processing.

[0071] Based on the above technical solution, when the first indication information is used to indicate the correct reception of the first information, the first indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating the correct reception of the first information through the first indication information, the recipient of the first indication information can trigger the corresponding AI processing based on the first indication information.

[0072] In a possible implementation manner of the second aspect, the first indication information is further used to indicate whether to perform processing based on the first data.

[0073] Optionally, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first data may be data obtained based on the second data. To this end, the first indication information may also indicate whether to process based on the first data, which can be understood as indicating whether the first communication device further processes the first data based on the data after the second processing and label data to obtain gradient data.

[0074] Optionally, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data, the second data may be data obtained based on the first data. To this end, the first indication information may also indicate whether to perform processing based on the first data, which can be understood as indicating whether the first communication device performs gradient update processing based on the first data (i.e., gradient data).

[0075] Based on the above technical solution, the first indication information is used not only to indicate whether the first information is correctly received, but also to indicate whether to process based on the first data. In this way, the first indication information can be reused to implement more indications to reduce overhead.

[0076] In a possible implementation manner of the second aspect, after the second communication device receives the second information, the method further includes: the second communication device sends second indication information, where the second indication information is used to indicate whether the second information is received correctly.

[0077] Based on the above technical solution, after the second communication device receives the second information, the second communication device may also send second indication information, so that the first communication device can determine whether the second communication device correctly receives the second information based on the second indication information, and subsequently the first communication device can determine whether to retransmit the second information and / or the second data scheduled by the second information based on the first indication information.

[0078] In a possible implementation manner of the second aspect, the second indication information indicates that the second information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and label data of the first data, the second indication information is also used to trigger the second processing; the second indication information indicates that the second information is correctly received, and when the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data of the second data, the second indication information is used to trigger the first processing.

[0079] Based on the above technical solution, when the second indication information is used to indicate the correct reception of the second information, the second indication information can also be used to trigger AI processing (for example, the first processing and / or the second processing). Thus, by indicating the correct reception of the second information through the second indication information, the recipient of the second indication information can trigger the corresponding AI processing based on the second indication information.

[0080] In a possible implementation manner of the second aspect, the second indication information is further used to indicate whether to perform processing based on the second data.

[0081] Optionally, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, the first data may be data obtained based on the second data. To this end, the second indication information may also indicate whether to perform processing based on the second data, which can be understood as indicating whether the first indication information may also indicate whether the second communication device performs gradient update processing based on the second data (i.e., gradient data).

[0082] Optionally, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second data may be data obtained based on the first data. To this end, the second indication information may also indicate whether to perform processing based on the second data, which can be understood as indicating whether the second communication device further processes the second data after the second processing and label data to obtain gradient data.

[0083] Based on the above technical solution, the second indication information is used not only to indicate whether the second information is correctly received, but also to indicate whether to process based on the second data. In this way, the second indication information can be reused to implement more indications to reduce overhead.

[0084] In a possible implementation manner of the second aspect, the second information is further used to indicate at least one of the following: a data type of the second data, and whether to send gradient information determined based on the second data.

[0085] Based on the above technical solution, the second information is also used to indicate at least one of the above items, so that the second communication device can obtain other information associated with the second data based on the second information, and assist in subsequent processing of the second data based on the other information.

[0086] In a possible implementation manner of the second aspect, the first information is further used to indicate at least one of the following: a data type of the first data, and whether to send gradient information determined based on the first data.

[0087] Based on the above technical solution, the first information is also used to indicate at least one of the above items, so that the recipient of the first information (i.e., the first communication device) can obtain other information associated with the first data based on the first information, and assist in subsequent processing of the first data based on the other information.

[0088] In a possible implementation manner of the second aspect, the method also includes: the second communication device receives capability information of the first communication device, and the capability information of the first communication device is used to determine the configuration information; wherein the AI ​​capability information of the first communication device includes at least one of the following: processing delay information of the first communication device for forward data of the AI ​​network structure to which the first data belongs, processing delay information of the first communication device for reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, load information of the processing resources of the first communication device, and computing power resource information of the first communication device.

[0089] Based on the above technical solution, the second communication device can receive the capability information of the first communication device, so that the second communication device determines the configuration information adapted to the capability information based on the capability information, so that the first communication device can receive the first information based on the configuration information.

[0090] Optionally, the configuration information includes at least one of the following: a period of the first information, a window length for detecting the first information, and a position of a transmission symbol of the first information in a time slot.

[0091] In a possible implementation manner of the second aspect, the method also includes: the second communication device sends third information, and the third information is used to indicate at least one of the following: information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

[0092] Based on the above technical solution, the second communication device may also send third information for indicating at least one of the above items, so that the first communication device can perform subsequent AI processing based on the third information.

[0093] The third aspect of the present application provides a communication device, which is a first communication device, and the device includes a transceiver unit and a processing unit; the transceiver unit is used to receive configuration information, the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit is used to receive the first data based on the first information; the transceiver unit is also used to send second information, and the second information is used to schedule the transmission of the second data; the processing unit is also used to send the second data based on the second information; wherein the first data is data after a first processing and the second data is gradient data obtained based on the first data after the second processing and label data, or the second data is data after a first processing and the first data is gradient data obtained based on the second data after the second processing and label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0094] In the third aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0095] The fourth aspect of the present application provides a communication device, which is a second communication device, and the device includes a transceiver unit and a processing unit, the transceiver unit is used to send configuration information, the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit is used to send the first data based on the first information; the transceiver unit is also used to receive second information, and the second information is used to schedule the transmission of the second data; the processing unit is also used to receive the second data based on the second information; wherein the first data is data after a first processing and the second data is gradient data obtained based on the first data after the second processing and label data, or the second data is data after a first processing and the first data is gradient data obtained based on the second data after the second processing and label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0096] In the fourth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

[0097] In a fifth aspect, the present application provides a communication device, comprising at least one processor, wherein the at least one processor is coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the method described in any possible implementation method of any one of the first to second aspects.

[0098] In a sixth aspect, the present application provides a communication device, comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in any possible implementation method of any one of the first to second aspects above.

[0099] A seventh aspect of the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0100] In an eighth aspect, the present application provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes a method as described in any possible implementation of any one of the first to second aspects above.

[0101] A ninth aspect of the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0102] In a tenth aspect, the present application provides a chip system, which includes at least one processor for supporting a communication device to implement the method described in any possible implementation of any one of the first to second aspects.

[0103] In a possible design, the chip system may also include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip, or may include a chip and other discrete devices. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data for the at least one processor.

[0104] Among them, the technical effects brought about by any design method in the third aspect to the tenth aspect can refer to the technical effects brought about by the different design methods in the above-mentioned first aspect to the second aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1a to Figure 1c A schematic diagram of a communication system provided for this application;

[0106] Figure 1d , Figure 1e as well as Figure 2a to Figure 2f A schematic diagram of the AI ​​processing process involved in this application;

[0107] Figure 3 An interactive schematic diagram of the communication method provided by this application;

[0108] Figure 4a , Figure 5 and Figure 6 A schematic diagram of the AI ​​processing process provided for this application;

[0109] Figure 4b to Figure 4g An interactive schematic diagram of the communication method provided by this application;

[0110] Figures 7 to 11 Schematic diagram of the AI ​​processing process provided for this application. DETAILED DESCRIPTION

[0111] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0112] (1) Terminal device: It can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and / or data connectivity to users, or a handheld device with wireless connection function, or other processing devices connected to a wireless modem.

[0113] The terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can be a mobile terminal device, such as a mobile phone (or "cellular" phone, mobile phone), a computer and a data card, for example, a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device, which exchanges voice and / or data with the radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablet computers (Pads), computers with wireless transceiver functions, and other devices. The wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile station (MS), a remote station, an access point (AP), a remote terminal device (remote terminal), an access terminal device (access terminal), a user terminal device (user terminal), a user agent (user agent), a subscriber station (SS), a customer premises equipment (CPE), a terminal (terminal), a user equipment (UE), a mobile terminal (MT), etc.

[0114] As an example but not limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for the application of wearable technology to intelligently design and develop wearable devices for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also powerful functions achieved through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include full-featured, large-size, and independent of smartphones to achieve complete or partial functions, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various types of smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0115] The terminal may also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle to everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc.

[0116] In addition, the terminal device may also be a terminal device in a communication system that evolves after the fifth generation (5th generation, 5G) communication system (e.g., a sixth generation (6th generation, 6G) communication system, etc.) or a terminal device in a public land mobile network (PLMN) that evolves in the future, etc. Exemplarily, the 6G network can further expand the form and function of the 5G communication terminal, and the 6G terminal includes but is not limited to a car, a cellular network terminal (with integrated satellite terminal function), a drone, and an Internet of Things (IoT) device.

[0117] In an embodiment of the present application, the terminal device may also obtain AI services provided by the network device. Optionally, the terminal device may also have AI processing capabilities.

[0118] (2) Network equipment: It can be equipment in a wireless network, for example, the network equipment can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station. At present, some examples of RAN equipment are: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point AP, etc. In addition, in a network structure, the network equipment may include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0119] Optionally, the RAN node may also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. The RAN node may also be a server, a wearable device, a vehicle or an onboard device, etc. For example, the access network device in the vehicle to everything (V2X) technology may be a road side unit (RSU).

[0120] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a centralized unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be set separately, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU) or a remote radio head (RRH).

[0121] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open access network (open RAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, CU, CU-CP, CU-UP, DU and RU are described as examples in this application. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0122] The communication between the access network device and the terminal device follows a certain protocol layer structure. The protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, or a physical (PHY) layer. The user plane protocol layer may include at least one of the following: a service data adaptation protocol (SDAP) layer, a PDCP layer, an RLC layer, a MAC layer, or a physical layer.

[0123] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, refer to Table 1 below.

[0124] Table 1

[0125] ORAN network elements 3GPP protocol layer functions O-CU-CP RRC+PCDP-Control Plane (PDCP-C) O-CU-UP SDAP+PCDP-User Plane (PDCP-U) O-DU RLC+MAC+PHY-high O-RU PHY-low

[0126] The network device may be any other device that provides wireless communication functions for the terminal device. The embodiments of the present application do not limit the specific technology and specific device form used by the network device. For the convenience of description, the embodiments of the present application do not limit.

[0127] The network equipment may also include core network equipment, such as mobility management entity (MME), home subscriber server (HSS), serving gateway (S-GW), policy and charging rules function (PCRF), public data network gateway (PDN gateway, P-GW) in the fourth generation (4G) network; access and mobility management function (AMF), user plane function (UPF) or session management function (SMF) and other network elements in the 5G network. In addition, the core network equipment may also include other core network equipment in the 5G network and the next generation network of the 5G network.

[0128] In an embodiment of the present application, the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it may be an AI node on the network side (access network or core network), a computing node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

[0129] In the embodiment of the present application, the device for realizing the function of the network device may be a network device, or may be a device capable of supporting the network device to realize the function, such as a chip system, which may be installed in the network device. In the technical solution provided in the embodiment of the present application, the technical solution provided in the embodiment of the present application is described by taking the device for realizing the function of the network device as an example that the network device is used as the device.

[0130] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used at the same time. Configuration refers to the network device / server sending some parameter configuration information or parameter values ​​to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values ​​or information. Pre-configuration is similar to configuration, and can be parameter information or parameter values ​​pre-negotiated between the network device / server and the terminal device, or parameter information or parameter values ​​used by the base station / network device or terminal device specified by the standard protocol, or parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0131] Furthermore, these values ​​and parameters can be changed or updated.

[0132] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated with each other are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

[0133] (5) "Send" and "receive" in the embodiments of the present application indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information is XX, which can include direct sending through the air interface, and also include indirect sending through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information is YY, which can include direct receiving from YY through the air interface, and also include indirect receiving from YY through the air interface from other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0134] In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules, or hardware modules within the device through a bus, wiring, or interface.

[0135] It is understandable that information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

[0136] (6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance. For example, the indication of specific information may be realized by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that, for the sender of the indication information, the indication information may be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information may be used to determine the information to be indicated.

[0137] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments in this application, and the various methods / designs / implementations in each embodiment, if there is no special description and logical conflict, the terms and / or descriptions between different embodiments and the various methods / designs / implementations in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various methods / designs / implementations in each embodiment can be combined to form new embodiments, methods, or implementations according to their inherent logical relationships. The implementation methods of this application described below do not constitute a limitation on the scope of protection of this application.

[0138] The present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a communication system evolved after 5G (such as 6G, etc.). The communication system includes at least one network device and / or at least one terminal device.

[0139] See also Figure 1a , which is a schematic diagram of a communication system in this application. Figure 1a In the example, a network device and six terminal devices are shown, and the six terminal devices are terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. Figure 1a In the example shown, terminal device 1 is a smart tea cup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas station, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0140] like Figure 1a As shown, the AI ​​configuration information sending entity may be a network device. The AI ​​configuration information receiving entity may be terminal devices 1-6. At this time, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 may send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device may send configuration information to terminal devices 1-6.

[0141] For example, in Figure 1a In the communication system, terminal device 4-terminal device 6 can also form a communication system. Among them, terminal device 5 acts as a network device, that is, an AI configuration information sending entity; terminal device 4 and terminal device 6 act as terminal devices, that is, AI configuration information receiving entities. For example, in the Internet of Vehicles system, terminal device 5 sends AI configuration information to terminal device 4 and terminal device 6 respectively, and receives data sent by terminal device 4 and terminal device 6; correspondingly, terminal device 4 and terminal device 6 receive AI configuration information sent by terminal device 5, and send data to terminal device 5.

[0142] by Figure 1a Taking the communication system shown as an example, in addition to executing communication-related services, different devices (including between network devices and network devices, between network devices and terminal devices, and / or between terminal devices and terminal devices) may also execute AI-related services.

[0143] like Figure 1b As shown, taking the network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and communication-related services and AI-related services can also be performed between different terminal devices.

[0144] like Figure 1c As shown, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be performed between the TV and the mobile phone.

[0145] The technical solution provided by this application can be applied to wireless communication systems (such as Figure 1a , Figure 1b or Figure 1cThe system shown in the figure), for example, the communication system provided in the present application can introduce an AI network element to implement some or all AI-related operations. The AI ​​network element may also be referred to as an AI node, an AI device, an AI entity, an AI module, an AI model, or an AI unit, etc. The AI ​​network element may be a network element built into a communication system. For example, the AI ​​network element may be an AI module built into: an access network device, a core network device, a cloud server, or a network management (operation, administration and maintenance, OAM) to implement AI-related functions. The OAM may be a network management device for a core network device and / or a network management device for an access network device. Alternatively, the AI ​​network element may also be an independently set network element in the communication system. Optionally, the terminal or the chip built into the terminal may also include an AI entity to implement AI-related functions.

[0146] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0147] Artificial intelligence (AI) can give machines human intelligence, for example, it can allow machines to use computer hardware and software to simulate certain intelligent behaviors of humans. In order to realize artificial intelligence, machine learning methods can be used. In machine learning methods, the machine uses training data to learn (or train) to obtain a model. The model represents the mapping from input to output. The learned model can be used for reasoning (or prediction), that is, the model can be used to predict the output corresponding to a given input. Among them, the output can also be called an inference result (or prediction result).

[0148] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0149] Supervised learning uses machine learning algorithms to learn the mapping relationship from sample values ​​to sample labels based on the collected sample values ​​and sample labels, and uses AI models to express the learned mapping relationship. The process of training a machine learning model is the process of learning this mapping relationship. During the training process, the sample values ​​are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). After the mapping relationship is learned, the learned mapping can be used to predict new sample labels. The mapping relationship learned by supervised learning can include linear mapping or nonlinear mapping. According to the type of label, the learning task can be divided into classification task and regression task.

[0150] Unsupervised learning uses algorithms to discover the inherent patterns of samples based on the collected sample values. One type of algorithm in unsupervised learning uses the samples themselves as supervisory signals, that is, the model learns the mapping relationship from sample to sample, which is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predicted value and the sample itself. Self-supervised learning can be used in applications such as signal compression and decompression recovery. Common algorithms include autoencoders and adversarial generative networks.

[0151] Reinforcement learning is different from supervised learning. It is a type of algorithm that learns problem-solving strategies by interacting with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have clear "correct" action label data. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust the decision-making actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user according to the total system throughput fed back by the wireless network, and then expects to obtain a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the state of the environment and the better (e.g., optimal) decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action". Reinforcement learning training is achieved through iterative interaction with the environment.

[0152] Neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, neural network can theoretically approximate any continuous function, so that neural network has the ability to learn any mapping. Traditional communication systems require rich expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover implicit pattern structures from large data sets, establish mapping relationships between data, and obtain performance that is superior to traditional modeling methods.

[0153] The idea of ​​neural networks comes from the neuron structure of the brain. For example, each neuron performs a weighted sum operation on its input values ​​and outputs the operation result through an activation function.

[0154] like Figure 1d As shown in Figure 1, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x = [x0, x1, ..., x n ], and the weights corresponding to each input are w=[w,w1,…,w n ], where n is a positive integer, w i and x i It can be a decimal, an integer (such as 0, a positive integer or a negative integer, etc.), or a complex number. i As x i The weight of xi Weighted. The bias of weighted summation of input values ​​according to the weight is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), the output of the neuron is: For another example, the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

[0155] In addition, a neural network generally includes multiple layers, each of which may include one or more neurons. By increasing the depth and / or width of a neural network, the expressive power of the neural network can be improved, providing a more powerful information extraction and abstract modeling capability for complex systems. Among them, the depth of a neural network may refer to the number of layers included in the neural network, and the number of neurons included in each layer may be referred to as the width of the layer. In one implementation, the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the output layer, and the output layer obtains the output result of the neural network. In another implementation, the neural network includes an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the middle hidden layer, the hidden layer calculates the received processing results, obtains the calculation results, and the hidden layer passes the calculation results to the output layer or the next adjacent hidden layer, and finally the output layer obtains the output result of the neural network. Among them, a neural network may include one hidden layer, or include multiple hidden layers connected in sequence, without limitation.

[0156] The neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, DNN may include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0157] Figure 1e This is a schematic diagram of an FNN network. The characteristic of the FNN network is that the neurons in adjacent layers are fully connected to each other. This characteristic makes FNN usually require a large amount of storage space and leads to high computational complexity.

[0158] CNN is a neural network that is specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling on the time axis) and image data (discrete sampling on two dimensions) can be considered to be data with a grid-like structure. CNN does not use all the input information for calculations at once, but uses a fixed-size window to intercept part of the information for convolution operations, which greatly reduces the amount of calculation of model parameters. In addition, depending on the type of information intercepted by the window (for example, people and objects in a picture are different types of information), each window can use different convolution kernel operations, which enables CNN to better extract the features of the input data.

[0159] RNN is a type of DNN network that uses feedback time series information. Its input includes the new input value at the current moment and its own output value at the previous moment. RNN is suitable for obtaining sequence features that are correlated in time, and is particularly suitable for applications such as speech recognition and channel coding.

[0160] In the above machine learning model training process, a loss function can be defined. The loss function describes the gap or difference between the output value of the model and the ideal target value. The loss function can be expressed in many forms, and there is no restriction on the specific form of the loss function. The model training process can be regarded as the following process: by adjusting some or all parameters of the model, the value of the loss function is less than the threshold value or meets the target requirements.

[0161] Models can also be referred to as AI models, rules or other names. AI models can be considered as specific methods for implementing AI functions. AI models characterize the mapping relationship or function between the input and output of a model. AI functions may include one or more of the following: data collection, model training (or model learning), model information publishing, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or reasoning result publishing, etc. AI functions can also be referred to as AI (related) operations, or AI-related functions.

[0162] The implementation process of the neural network will be described exemplarily below with reference to the accompanying drawings.

[0163] 1. Fully connected neural network, also called multilayer perceptron (MLP).

[0164] like Figure 2a As shown in the figure, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (middle). Each layer of the MLP contains several nodes, called neurons. The neurons in two adjacent layers are connected to each other.

[0165] Optionally, considering the neurons of two adjacent layers, the output h of the neurons in the next layer is the weighted sum of all the neurons x in the previous layer connected to it and passes through the activation function, which can be expressed as:

[0166] h=f(wx+b).

[0167] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0168] Alternatively, the output of the neural network can be recursively expressed as:

[0169] y=f n (w n f n-1 (…)+b n ).

[0170] Among them, n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0171] In other words, a neural network can be understood as a mapping relationship from an input data set to an output data set. Usually, neural networks are randomly initialized, and the process of obtaining this mapping relationship from random w and b using existing data is called neural network training.

[0172] Optionally, a specific method of training is to use a loss function to evaluate the output results of the neural network.

[0173] like Figure 2b As shown, the error can be back-propagated, and the neural network parameters (including w and b) can be iteratively optimized by the gradient descent method until the loss function reaches the minimum value, that is, Figure 2b The term "better point (e.g., optimal point)" is used in the context of Figure 2b The neural network parameters corresponding to the “better point (e.g., optimal point)” in the training can be used as the neural network parameters in the trained AI model information.

[0174] Alternatively, the gradient descent process can be expressed as:

[0175]

[0176] Among them, θ is the parameter to be optimized (including w and b), L is the loss function, η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, It means taking the derivative of θ with respect to L.

[0177] Optionally, the back-propagation process utilizes the chain rule for partial derivatives.

[0178] like Figure 2c As shown, the gradient of the previous layer parameters can be recursively calculated by the gradient of the next layer parameters, which can be expressed as:

[0179]

[0180] Among them, w ij is the weight of node j connecting node i, s i is the weighted sum of the inputs to node i.

[0181] 2. Federated Learning (FL)

[0182] The concept of federated learning effectively solves the current difficulties faced by the development of artificial intelligence. On the premise of fully protecting user data privacy and security, it efficiently completes the model learning tasks by promoting the collaboration of various edge devices and central servers.

[0183] like Figure 2d As shown in the figure, the FL architecture is the most widely used training architecture in the current FL field. The FedAvg algorithm is the basic algorithm of FL. Its algorithm flow is as follows:

[0184] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0185] (2) In round t∈[1,T], client k∈[1,K] based on the local dataset For the received global model Perform E epochs of training to obtain local training results Report it to the central node.

[0186] (3) The central node aggregates and collects local training results from all (or some) clients. Assume that the client set that uploads the local model in round t is The center will use the number of samples of the corresponding client as the weight to perform weighted averaging to obtain a new global model. The specific update rule is: The center then sends the latest version of the global model Broadcast to all client devices for a new round of training.

[0187] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0188] In addition to reporting local models You can also use the local gradient of the training After reporting, the central node averages the local gradients and updates the global model according to the direction of the average gradient.

[0189] As you can see, in the FL framework, the data set exists in the distributed nodes, that is, the distributed nodes collect local data sets, perform local training, and report the local results (models or gradients) obtained from the training to the central node. The central node itself does not have a data set, and is only responsible for fusing the training results of the distributed nodes to obtain the global model and send it to the distributed nodes.

[0190] 3. Decentralized learning: Different from federated learning, there is another distributed learning architecture - decentralized learning.

[0191] like Figure 2e As shown in Figure 2, consider a fully distributed system without a central node. The design goal f(x) of a decentralized learning system is generally the goal f of each node. i The mean of (x), that is Where n is the number of distributed nodes, x is the parameter to be optimized. In machine learning, x is the parameter of the machine learning (such as neural network) model. Each node uses local data and local target f i (x) Calculate local gradient Then it is sent to the neighboring nodes that can be communicated with. After any node receives the gradient information sent by its neighbor, it can update the parameter x of the local model according to the following formula:

[0192]

[0193] in, represents the parameters of the local model after the k+1th (k is a natural number) update in the i-th node, represents the parameters of the local model after the kth update in the i-th node (if k is 0, it means is the parameter of the local model of the i-th node that does not participate in the update), α k Represents the tuning coefficient, N i is the set of neighbor nodes of node i, |N i | represents the number of elements in the neighbor node set of node i, that is, the number of neighbor nodes of node i. Through information interaction between nodes, the decentralized learning system will eventually learn a unified model.

[0194] The technical solution provided by this application can be applied to wireless communication systems (such as Figure 1a or Figure 1b In the wireless communication system, communication nodes generally have signal transceiving capabilities and computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computing support for signal transceiving capabilities (for example, sending and receiving signals) to achieve communication tasks between network devices and other communication nodes.

[0195] In a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently.

[0196] In one possible implementation, the communication node can be used as a participating node of the AI ​​learning system, and the computing power of the communication node is applied to a certain link of the AI ​​learning system. With the advent of the era of large models, deep learning models with massive parameters, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformer (GPT-2), can complete more and more complex tasks and achieve better performance. However, for large models, even the reasoning process of the model will be limited by the device capacity, so generally large models are stored on cloud central servers. At the same time, each device in the network generates a huge amount of raw data every day, which requires multiple calls to the large model for reasoning. Generally speaking, the device (such as a communication node) can send data to the central server, the central server uses the data for reasoning, and then the central server returns the reasoning result to the device. This process will consume a lot of communication resources for data transmission, and the privacy of device data will also be at risk.

[0197] In order to better save communication overhead and protect the privacy of user data, scholars have proposed distributed reasoning technology for deep neural networks. The approach is to distribute the model to devices and use the local computing power of the devices to infer the model, thereby reducing communication overhead and obtaining data privacy protection.

[0198] For example, in Figure 2f In the example shown, it is taken that two communication nodes, Node 1 and Node 2, participate in the AI ​​learning system. Wherein, both Node 1 and Node 2 can be communication nodes, such as terminal devices or network devices. Wherein, the neural network used by the AI ​​learning system can include at least a sub-neural network deployed at Node 1 for AI encoding, and / or, a sub-neural network deployed at Node 2 for AI decoding.

[0199] As Figure 2fAn implementation example is shown in which node 1 processes the encoding result based on the sub-neural network for AI encoding, and the encoding result is quantized and processed at the physical layer to obtain a wireless signal; correspondingly, after node 2 receives the wireless signal through the transmission of the wireless channel, node 2 processes the wireless signal at the physical layer and dequantizes the signal, and obtains the decoding result after AI decoding. In addition, node 2 can also determine the gradient data based on the decoding result and the label data.

[0200] Afterwards, after node 2 obtains gradient data based on the sub-neural network processing of AI decoding, the gradient data is quantized and processed at the physical layer to obtain a wireless signal; correspondingly, after node 1 receives the wireless signal through transmission through the wireless channel, node 1 obtains gradient data after physical layer processing and dequantization processing. Subsequently, node 1 can optimize the neural network (such as training / updating / iteration, etc.) of the sub-neural network for AI encoding deployed in node 1 based on the gradient data.

[0201] Optionally, after node 2 obtains the gradient data, node 2 can also optimize the sub-neural network for AI encoding deployed in node 2 based on the gradient data (e.g., training / updating / iteration, etc.).

[0202] It should be noted that the node 2 can also calculate the result of the loss function based on the decoding result and the label data, and the result of the loss function can also be used for optimizing the neural network. The above implementation is only explained by taking the node 2 determining the gradient data as an example.

[0203] However, in the above implementation process, the processing of the neural network (such as the processing process of the sub-neural network for AI encoding deployed in node 1, the sub-neural network for AI decoding deployed in node 2, etc.) and communication (such as physical layer processing) are two independent operations, which are completed at different protocol layers, requiring more steps and higher latency.

[0204] In order to solve the above problems, the present application provides a communication method and related equipment, which are used to enable the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment. The following will be described in detail with reference to the accompanying drawings.

[0205] See also Figure 3 , is a schematic diagram of an implementation of the communication method provided in this application, and the method includes the following steps.

[0206] It should be noted that in Figure 3In the example, the first communication device and the second communication device are used as the execution subjects of the interaction indication to illustrate the method, but the present application does not limit the execution subjects of the interaction indication. Figure 3 and later Figure 6 In the method, the execution subject can be replaced by a chip, a chip system, a processor, a logic module or software in a communication device. The first communication device can be a terminal device and the second communication device can be a network device, or the first communication device and the second communication device are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in a sidelink communication scenario).

[0207] S301. The second communication device sends configuration information, and correspondingly, the first communication device receives the configuration information, wherein the configuration information is used to configure transmission resources of first information, and the first information is used to schedule transmission of first data.

[0208] It should be understood that after the second communication device sends the configuration information for configuring the transmission resource of the first information in step S301, the second communication device can send the first information based on the configuration information, and correspondingly, the first communication device can receive the first information based on the configuration information (for example, Figure 3 Implementation process of step A in ).

[0209] S302. The second communication device sends first data, and correspondingly, the first communication device receives the first data.

[0210] S303. The first communication device sends second information, and correspondingly, the first communication device receives the second information, wherein the second information is used to schedule transmission of the second data.

[0211] S304. The first communication device sends the second data, and correspondingly, the first communication device receives the second data.

[0212] In this application, the terms AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0213] In the present application, the data involved (such as first data, second data, etc.) can be replaced by information, signals, etc.

[0214] It should be understood that in a wireless communication system, the first information used to schedule the first data and / or the second information used to schedule the second data can be messages / signaling / information of a radio resource control (RRC) layer, a medium access control (MAC) layer, a physical (PHY) layer or other protocol layers.

[0215] In addition, compared to the implementation method in which the communication device processes the received application layer data through the physical layer and then dequantizes the physical layer processing results to obtain the application layer scheduling signaling (the scheduling signaling is used to schedule the transmission of data associated with AI processing), when the first information used to schedule the first data and / or the second information used to schedule the second data is physical layer signaling, the transmission of data associated with AI processing can be quickly scheduled through physical layer signaling, thereby reducing the processing delay.

[0216] Exemplarily, take the example that the first information for scheduling the first data comes from the second communication device. The second communication device may be a network device, and accordingly, the first information may be downlink control information (DCI) sent by the network device to the terminal device, and the second information may be uplink control information (UCI) sent by the terminal device to the network device. Alternatively, the second communication device may be another terminal device different from the first communication device, and accordingly, the first information and the second information may be sidelink control information (SCI) exchanged between different terminal devices.

[0217] It should be noted that the first data may be data after the first processing and the second data may be gradient data obtained based on the data after the second processing and the label data of the first data, or the second data may be data after the first processing and the first data may be gradient data obtained based on the data after the second processing and the label data of the second data. In other words, the first data may be obtained based on the second data, or the second data may be obtained based on the first data. That is, the execution order of step S302 and step S304 may be in multiple ways, which will be introduced through some implementation examples below.

[0218] Implementation method 1: Step S302 is performed first and step S304 is performed later. In this case, the second data may be data obtained based on the first data. For example, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, the first data is data sent by the sender of the first data (e.g., the second communication device) after the first processing, and the second data is data obtained by the first communication device after the second processing based on the received first data.

[0219] In other words, in implementation mode 1, the first communication device performs the first processing to obtain the first data, and the second communication device performs the second processing to obtain the second data. In this case, the first data can be referred to as forward data, and the second data can be referred to as reverse data (e.g., reverse gradient, result of loss function, etc.).

[0220] It can be understood that, in implementation mode 1, step S302 is executed first and step S304 is executed later, while the first information for scheduling the first data is executed before step S302, and the second information for scheduling the second data (i.e., step S303) is executed before step S304. In addition, the execution order of the first data sending and receiving process in step S302 and the second information sending and receiving process in step S303 is not limited. For example, step S302 is executed first and step S303 is executed later; for another example, step S303 is executed first and step S302 is executed later.

[0221] In implementation mode 2, step S304 is performed first and step S302 is performed later. In this case, the first data may be data obtained based on the second data. For example, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data, the second data is data sent by the first communication device after the first processing, and the first data is data obtained by the sender of the first data (e.g., the second communication device) performing the second processing based on the received second data.

[0222] In other words, in implementation mode 2, the second communication device performs the first processing to obtain the first data, and the first communication device performs the second processing to obtain the second data. In this case, the second data can be referred to as forward data, and the first data can be referred to as reverse data (e.g., reverse gradient, result of loss function, etc.).

[0223] It can be understood that, in the second implementation, step S304 is executed first and step S302 is executed later, and the first information for scheduling the first data is executed before step S302, and the second information for scheduling the second data (i.e., step S303) is executed before step S304. In addition, the execution order of the second data transmission and reception process and the first information transmission and reception process in step S304 is not limited. For example, step S304 is executed first and the first information transmission and reception process is executed; for another example, the first information transmission and reception process is executed first and step S304 is executed later.

[0224] Optionally, in the above implementation mode 1 and implementation mode 2, the execution order between the sending and receiving process of the second information and the sending and receiving process of the first information in step S304 is not limited. For example, step S304 is executed first and the sending and receiving process of the first information is executed later; for another example, the sending and receiving process of the first information is executed first and step S304 is executed later.

[0225] It should be understood that in the above-mentioned implementation method 1 or implementation method 2, the first processing performed by the first communication device or the second communication device may include AI processing, and / or, the second processing performed by the first communication device or the second communication device may include AI processing. Exemplarily, if the first processing includes AI processing, the AI ​​processing in the first processing may be referred to as encoding neural network processing, AI encoder processing, AI encoding neural network processing, etc. Correspondingly, if the second processing includes AI processing, the AI ​​processing in the second processing may be referred to as decoding neural network processing, AI decoder processing, AI decoding neural network processing, etc.

[0226] It can be seen from the implementation process of the above implementation mode 1 and implementation mode 2 that after the first communication device receives the configuration information in step S301, there may be an association relationship between the first information received by the first communication device based on the configuration information and the second information sent by the first communication device in step S303. There may be multiple implementation modes for the association relationship, which will be exemplarily described below through implementation mode A and implementation mode B.

[0227] Implementation A: The time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

[0228] Specifically, in the configuration information received by the first communication device in step S301, the transmission resources of the first information include the time domain resources carrying the first information; wherein the time interval between the time domain resources carrying the first information and the time domain resources carrying the second information is preconfigured.

[0229] In this way, when the first information transmission and reception process is performed first and the second information transmission and reception process is performed later, after the first communication device determines the time domain resource carrying the first information based on the configuration information in step S301, the first communication device can receive the first information based on the time domain resource carrying the first information, and the first communication device can determine the time domain resource carrying the second information based on the preconfigured time interval and the time domain resource carrying the first information. Thereafter, after the first communication device receives the first information, the first communication device can implement the transmission of the second information on the time domain resource carrying the second information. In addition, the second communication device can also implement the reception of the second information based on the preconfigured time interval, which can reduce the resource configuration overhead of the second information.

[0230] Similarly, in the case where the second information transmission and reception process is performed first and the first information transmission and reception process is performed later, after the first communication device determines the time domain resource of the first information based on the configuration information in step S301, the first communication device can determine the time domain resource carrying the second information before the time domain resource carrying the first information based on the preconfigured time interval. Thereafter, after the first communication device can implement the transmission of the second information on the time domain resource carrying the second information, the first communication device receives the first information on the time domain resource carrying the first information. In addition, the second communication device can also implement the reception of the second information and the transmission of the first information based on the preconfigured time interval.

[0231] Optionally, implementation method A can be understood as a real-time data alignment method, where real-time can be understood as a process in which the first communication device receives the first information, and the time interval between the process in which the first communication device sends the second information is relatively fixed; and / or, a process in which the first communication device performs processing (for example, the first processing or the second processing) to obtain the second data, and the time interval between the process in which the second communication device performs processing (for example, the first processing or the second processing) to obtain the first data is relatively fixed.

[0232] Figure 4a This is an implementation example of implementation mode A (i.e., real-time data alignment mode). In this example, the first frame in every six frames (e.g., frames with frame numbers 1 / 7 / 13) is used to transmit the first information sent by the second communication device, and the fourth frame in every six frames (e.g., frames with frame numbers 4 / 10 / 16) is used to transmit the second information sent by the first communication device. In other words, the time interval between the time domain resources carrying the first information and the time domain resources carrying the second information can be preconfigured.

[0233] It is understandable that in Figure 4a In addition to exchanging configuration information, first information, first data, second information, and second data, the first communication device and the second communication device may also exchange other data, such as Figure 4a Other communication signals shown include, for example, system information, reference signals, channel information measured based on reference signals, etc.

[0234] In a possible implementation of implementation A, the first communication device may send indication information to the second communication device (or receive indication information from the second communication device), where the indication information is used to indicate the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information. In this way, the first communication device and the second communication device can align their understanding of the time interval to avoid transmission errors.

[0235] Optionally, when receiving indication information from the second communication device (the indication information is used to indicate the time interval), the indication information may be carried in the configuration information in step S301.

[0236] Implementation B: Any one of the first information and the second information and any one of the first processing and the second processing may trigger each other.

[0237] As an implementation example 1 of implementation manner B, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data (i.e., in the case of the above implementation manner 1), the second processing is triggered based on the first information and the first processing is triggered based on the second information, or the first information is triggered based on the first processing and the second information is triggered based on the second processing;

[0238] like Figure 4b In the implementation process shown, in the implementation example 1 of the implementation example B, when the second processing is triggered based on the first information and the first processing is triggered based on the second information, the process of sending and receiving the second information (i.e., step S303) is executed first and the process of sending and receiving the first data obtained by the first processing (i.e., step S302) is executed later, and the process of sending and receiving the first information is executed first and the process of sending and receiving the second data obtained by the second processing (i.e., step S304) is executed later.

[0239] like Figure 4c In the implementation process shown, in the implementation example 1 of the implementation example B, when the first information is triggered based on the first process and the second information is triggered based on the second process, the second communication device triggers the execution of the first information receiving and sending process in the process of obtaining the first data based on the first process, and then sends the first data (i.e., step S302). In addition, the first communication device triggers the execution of the second information receiving and sending process (i.e., step S303) in the process of obtaining the second data based on the second process, and then sends the second data (i.e., step S304).

[0240] As a second implementation example of implementation method B, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data (that is, in the case of the above-mentioned implementation method two), the second processing is triggered based on the second information and the first processing is triggered based on the first information, or, the first information is triggered based on the second processing and the second information is triggered based on the first processing.

[0241] like Figure 4dIn the implementation process shown, in the implementation example 2 of the implementation example B, when the second process is triggered based on the second information and the first process is triggered based on the first information, after receiving the first information, the first communication device triggers the execution of obtaining the second data based on the first process, and executes step S304. Correspondingly, after receiving the second information in step S303, the second communication device triggers the execution of obtaining the first data based on the second process, and executes step S302.

[0242] like Figure 4e In the implementation process shown, in implementation example 2 of implementation example B, when the second information is triggered based on the first processing and the first information is triggered based on the second processing, the first communication device triggers the process of sending the second information in step S303 during the process of obtaining the second data based on the first processing; in addition, the second communication device triggers the process of sending the first information during the process of obtaining the first data based on the second processing.

[0243] In the above implementation example 1 and implementation example 2, since the first processing includes AI processing, and / or the second processing includes AI processing, the above implementation method can trigger the scheduling of AI data through AI processing, or the above implementation method can trigger AI processing through the scheduling of AI data. Thus, the AI ​​processing and the scheduling of AI data can trigger each other, thereby reducing the interaction of the trigger indication of the AI ​​processing or the trigger indication of the scheduling of AI data, which can reduce the processing delay and reduce the overhead.

[0244] As implementation example three of implementation method B, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data (that is, in the case of the above-mentioned implementation method one), the second processing is triggered based on the first data.

[0245] like Figure 4f In the implementation process shown, in implementation example three of implementation example B, when the second processing is triggered based on the first data, after the first communication device receives the first data in step S302, the first communication device triggers execution based on the second processing to obtain the second data.

[0246] Optionally, in implementation example three, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data, that is, after the first communication device confirms receipt of the first information and the first data, the first communication device triggers execution based on the second processing to obtain the second data.

[0247] As implementation example 4 of implementation method B, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data (that is, in the case of the above-mentioned implementation method 2), the second processing is triggered based on the second data.

[0248] like Figure 4g In the implementation process shown, in implementation example 4 of implementation example B, when the second processing is triggered based on the second data, after the second communication device receives the second data in step S304, the second communication device triggers the execution of obtaining the first data based on the second processing.

[0249] Optionally, in implementation example four, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data, that is, after the second communication device confirms receipt of the second information and the second data, the second communication device triggers execution based on the first processing to obtain the first data.

[0250] In the above implementation examples 3 and 4, since the first processing includes AI processing, and / or the second processing includes AI processing, the above implementation can trigger AI processing through the transmission of AI data. Thus, AI processing and AI data transmission can trigger each other, thereby reducing the interaction of trigger indications for AI processing, reducing processing delay and reducing overhead.

[0251] Figure 5 This is an example of a scenario for implementing method B, in which the implementation scenarios of the above-mentioned implementation examples 1 and 3 are taken as examples. That is, the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data. In addition, in this example, the first communication device is a terminal device for performing the second processing and the second communication device is a network device for performing the first processing, that is, the first information is DCI and the second information is UCI.

[0252] Depend on Figure 5It can be seen from the implementation process that in the above implementation example one, after the network device sends DCI through the downlink transmission link, the terminal device can receive the DCI through the downlink reception link, and the terminal device can trigger the second processing based on the DCI; similarly, after the terminal device sends UCI through the uplink transmission link, the network device can receive the UCI through the uplink reception link, and the network device can trigger the first processing based on the UCI. In the above implementation example three, after the network device sends the first data through the downlink transmission link, the terminal device can receive the first data through the downlink reception link, and the terminal device can trigger the second processing based on the first data; similarly, after the terminal device sends the second data through the uplink transmission link, the network device can receive the second data through the uplink reception link, and the network device can trigger the first processing based on the second data.

[0253] Optionally, implementation B can be understood as a data synchronization method in a non-real-time system. The non-real-time here can be understood as the time interval between the process of the first communication device receiving the first information and the process of the first communication device sending the second information is not relatively fixed, and / or the time interval between the process of the first communication device performing a process (such as the first process or the second process) to obtain the second data and the process of the second communication device performing a process (such as the first process or the second process) to obtain the first data is not relatively fixed.

[0254] Figure 6 This is an implementation example of implementation method B (i.e., non-real-time data alignment method). In this example, multiple AI tasks can be executed between the first communication device and the second communication device, and the execution cycles of different AI tasks or the triggering of data transmission and reception of different AI tasks may be different. For example, the scale of the first data of different AI tasks may be different. For another example, the scale of the second data of different AI tasks may be different.

[0255] exist Figure 6 In the example shown, the scheduling information involved in one AI task may include the first information transmitted on the time resource with a frame number of 1 and the second information transmitted on the time resource with a frame number of 4, that is, the interval between the two is 2 frames (that is, frames with frame numbers 2 and 3); the data involved in another AI task may include the first information transmitted on the time resource with a frame number of 5 and the second information transmitted on the time resource with a frame number of 10, that is, the interval between the two is 4 frames (that is, frames with frame numbers 6, 7, 8 and 9); the data involved in another AI task may include the first information transmitted on the time resource with a frame number of 17 and the second information transmitted on the time resource with a frame number of 18, that is, the interval between the two is 0 frames (that is, the two are two adjacent frames).

[0256] In a possible implementation of implementation B, the configuration information received by the first communication device in step S301 includes a first configuration and a second configuration, the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information; wherein the time length of the cycle corresponding to the search space is less than or equal to the time length of the retransmission timer. In this way, the first communication device can receive the first information and retransmit the second information based on the configuration information, thereby improving the transmission success rate of the first information and the second information.

[0257] In addition, since the first information may be mutually triggered with the AI ​​processing, that is, the first information may be executed irregularly. For this reason, by implementing the time length of the period corresponding to the search space less than or equal to the time length of the retransmission timer, the first communication device can detect the first information in a period of shorter time length, so that the first communication device can timely receive the data after AI processing or timely trigger the AI ​​processing. Moreover, the timer with a longer time length can reduce the overhead of the first communication device retransmitting the second information.

[0258] Implementation C: The first information and the first data may trigger each other, and / or the second information and the second data may trigger each other.

[0259] As an implementation example of implementation mode C, the process of the second communication device sending the first information can be used to trigger the generation of the first data or the sending of the first data. Similarly, the process of the first communication device sending the second information can be used to trigger the generation of the second data or the sending of the second data.

[0260] As another implementation example of implementation mode C, the process of the second communication device generating or sending the first data can trigger the process of the second communication device sending the first information. Similarly, the process of the first communication device generating or sending the second data can trigger the process of the first communication device sending the second information.

[0261] It should be noted that the triggering process in implementation method C can refer to the description of implementation method B above (for example Figure 4b to Figure 4g Implementation example in ).

[0262] In a possible implementation, after the first communication device receives the first information based on the configuration information in step S301, the method further includes: the first communication device sends first indication information, where the first indication information is used to indicate whether the first information is correctly received.

[0263] In the present application, whether it is correctly received can be replaced by other terms, including but not limited to: whether it is incorrectly received, whether it is correctly parsed, whether it is incorrectly parsed, etc. Specifically, the first communication device can also send first indication information, so that the second communication device can clarify whether the first communication device correctly receives the first information based on the first indication information, and subsequently the second communication device can determine whether to retransmit the first information and / or the first data scheduled by the first information based on the first indication information.

[0264] In addition, the first information is used to schedule the first data. Generally, the receiver of the first information and the first data feeds back whether the first data is correctly received, and the receiver does not feed back whether the first information is correctly received. In the above technical solution, if the first information can be used to trigger AI processing (such as the first processing and / or the second processing), for this purpose, the receiver of the first indication information can clearly know whether the first communication device triggers the corresponding AI processing based on the first information by indicating whether the first information is correctly received through the first indication information.

[0265] Optionally, it can be seen from the above implementation process that there may be an association relationship between the first information received by the first communication device based on the configuration information and the second information sent by the first communication device in step S303. In addition to the above implementation methods A and B, there may be other implementation methods for this association relationship.

[0266] For example, if the first indication information is used to indicate the correct receipt of the first information, when the first data is data after the first processing and the second data is gradient data obtained based on the first data after the second processing and the label data (that is, in the case of the above-mentioned implementation method one), the first indication information is also used to trigger the first processing.

[0267] For another example, if the first indication information is used to indicate that the first information is correctly received, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data (i.e., in the case of the above-mentioned implementation method 2), the first indication information is also used to trigger the second processing. Specifically, when the first indication information is used to indicate that the first information is correctly received, the first indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing).

[0268] It should be noted that the implementation process of triggering the first process or the second process by the first indication information can refer to the process of triggering the first process or the second process by the first information or the second information mentioned above (for example Figure 4b to Figure 4d implementation process shown).

[0269] Therefore, by indicating the correct reception of the first information through the first indication information, the recipient of the first indication information can trigger corresponding AI processing based on the first indication information.

[0270] In a possible implementation manner, the first indication information is also used to indicate whether to perform processing based on the first data.

[0271] Optionally, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data (i.e., in the case of the above-mentioned implementation method 1), the first data may be data obtained based on the second data. To this end, the first indication information may also indicate whether to process based on the first data, which can be understood as indicating whether the first communication device further processes the data after the second processing of the first data and label data to obtain gradient data.

[0272] Optionally, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data (that is, in the case of the above-mentioned implementation method two), the second data may be data obtained based on the first data. To this end, the first indication information may also indicate whether to process based on the first data. It can be understood that the first indication information may also indicate whether the first communication device performs gradient update processing based on the first data (that is, gradient data). Specifically, in addition to indicating whether the first information is correctly received, the first indication information is also used to indicate whether to process based on the first data. In this way, the first indication information can be reused to implement more indications to reduce overhead.

[0273] In a possible implementation, after the first communication device sends the second information in step S303, the method further includes: the first communication device receives second indication information, and the second indication information is used to indicate whether the second information is correctly received. Specifically, after the first communication device sends the second information, the first communication device may also receive the second indication information, so that the first communication device determines whether the second communication device correctly receives the second information based on the second indication information, and subsequently the first communication device may determine whether to retransmit the second information and / or the second data scheduled by the second information based on the first indication information.

[0274] Optionally, it can be seen from the above implementation process that there may be an association relationship between the first information received by the first communication device based on the configuration information and the second information sent by the first communication device in step S303. In addition to the above implementation methods A and B, there may be other implementation methods for this association relationship.

[0275] For example, if the second indication information is used to indicate the correct receipt of the second information, when the first data is data after the first processing and the second data is gradient data obtained based on the first data after the second processing and the label data (that is, in the case of the above-mentioned implementation method one), the second indication information is also used to trigger the second processing.

[0276] For another example, if the second indication information is used to indicate the correct receipt of the second information, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data (that is, in the case of the above-mentioned implementation method two), the second indication information is used to trigger the first processing.

[0277] It should be noted that the implementation process of triggering the first process or the second process by the second indication information can refer to the process of triggering the first process or the second process by the first information or the second information mentioned above (for example Figure 4b to Figure 4d implementation process shown).

[0278] Thus, in the case where the second indication information is used to indicate that the second information is correctly received, the second indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating that the second information is correctly received through the second indication information, the recipient of the second indication information can trigger corresponding AI processing based on the second indication information.

[0279] In a possible implementation, if the first communication device receives the second indication information, the second indication information is used not only to indicate whether the second information is correctly received, but also to indicate whether to perform processing based on the second data.

[0280] In an implementation example, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data (i.e., in the case of the above-mentioned implementation method 1), the first data may be data obtained based on the second data. To this end, the second indication information may also indicate whether to perform processing based on the second data, which can be understood as indicating whether the first indication information may also indicate whether the second communication device performs gradient update processing based on the second data (i.e., gradient data).

[0281] In another implementation example, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data (i.e., in the case of the above-mentioned implementation method 2), the second data may be data obtained based on the first data. To this end, the second indication information may also indicate whether to process based on the second data, which can be understood as indicating whether the second communication device further processes the second data after the second processing and the label data to obtain gradient data.

[0282] Therefore, in addition to indicating whether the second information is correctly received, the second indication information is also used to indicate whether to perform processing based on the second data. In this way, the second indication information can be reused to implement more indications to reduce overhead.

[0283] In a possible implementation, the second information sent by the first communication device in step S303 is also used to indicate at least one of the following: the data type of the second data, whether to send gradient information determined based on the second data. Specifically, the second information is also used to indicate at least one of the above items, so that the recipient of the second information (i.e., the second communication device) can obtain other information associated with the second data based on the second information, and assist in subsequent processing of the second data based on the other information.

[0284] In a possible implementation, the first information configured by the configuration information received by the first communication device in step S301 is also used to indicate at least one of the following: the data type of the first data, whether to send gradient information determined based on the first data. Specifically, the first information is also used to indicate at least one of the above items, so that the receiver of the first information (i.e., the first communication device) can obtain other information associated with the first data based on the first information, and assist in subsequent processing of the first data based on the other information.

[0285] In a possible implementation, after the first communication device receives the first information based on the configuration information in step S301, the method further includes: in the case where the first information is parsed incorrectly, the first communication device determines not to receive the first data. Optionally, after the first communication device receives the first information, the method further includes: in the case where the first information is parsed incorrectly, the first communication device does not expect to receive the first data. Specifically, in the case where the first information is parsed incorrectly, the first communication device may determine not to receive the first data to avoid receiving erroneous data.

[0286] In a possible implementation, before step S301, the method further includes: the first communication device sends capability information of the first communication device, and the capability information of the first communication device is used to determine the configuration information; wherein the AI ​​capability information of the first communication device includes at least one of the following: processing delay information of the first communication device for forward data of the AI ​​network structure to which the first data belongs, processing delay information of the first communication device for reverse data in the AI ​​network structure to which the first data belongs, batch size of the first data, load information of processing resources of the first communication device, and computing power resource information of the first communication device. Specifically, the first communication device can send the capability information of the first communication device, so that the second communication device determines the configuration information adapted to the capability information based on the capability information, so that the first communication device can receive the success rate of the first information based on the configuration information.

[0287] Optionally, the configuration information includes at least one of the following: a period of the first information, a window length for detecting the first information, and a position of a transmission symbol of the first information in a time slot. Exemplarily, the configuration information may include a first configuration, and the at least one item may be included in the first configuration, and the first configuration is used to configure a search space for the first information.

[0288] As an implementation example, take the first communication device as a terminal device and the second communication device as a network device as an example. That is, the terminal device may receive configuration information in step S301, and the terminal device may send capability information before step S301, and the capability information may be used to determine the configuration information. Among them, the network device may receive capability information of one or more terminal devices, and send configuration information to the one or more terminal devices respectively.

[0289] Exemplarily, the network device may save a mapping relationship between the terminal device capability and the resource of the first information configured by the configuration information, as shown in Table 2, taking the resource of the first information configured by the configuration information as the search space of the DCI as an example.

[0290] Table 2

[0291]

[0292] Among them, the capability index can correspond to different capabilities of the terminal device, for example, different capability indexes can represent the computing power level of the device, computing latency, etc.; the task identifier can be a task index (Task index), which can correspond to different tasks, different neural network structures, or different model complexities, etc. In addition, searchSpaceId x (in the example shown in Table 2, x ranges from 0 to 7) indicates a specific searchSpace configuration. An example configuration of searchSpace is shown in Table 3 below.

[0293] Table 3

[0294]

[0295] It should be understood that in Table 3, the searchSpace configuration may include one or more fields in Table 3. In Table 3, the definition of some information elements is as follows:

[0296] The "monitoringSlotPeriodicityAndOffset" element indicates the monitoring period (ie, the period of the first information), sl160 indicates 160 slots, and the value indicates the offset within the 160 slots.

[0297] The "Duration" information element indicates the duration of the monitoring (ie, the window duration for detecting the first information).

[0298] The "monitoringSymbolsWithinSlot" information element indicates the symbol number within the monitoring slot from which the monitoring starts (ie, the position of the transmission symbol of the first information in the time slot).

[0299] Optionally, for the same terminal device, the network device may configure different searchSpace configurations according to different training tasks / neural network structures, that is, the configuration information received by the terminal device in step S301 may include different searchSpace configurations, and the different searchSpace configurations correspond to different training tasks, or the searchSpace configurations correspond to different neural network structures.

[0300] In one possible implementation, Figure 3 The method shown may also include: the first communication device receives third information, and the third information is used to indicate at least one of the following: information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs. Specifically, the first communication device may also receive third information indicating at least one of the above items, so that the first communication device can perform subsequent AI processing based on the third information (for example, when the second processing in the above implementation method 1 includes AI processing, or when the first processing in the above implementation method 2 includes AI processing).

[0301] based on Figure 3And related technical solutions, the first data is data after the first processing and the second data is gradient data obtained based on the first data after the second processing and label data, or the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data. And, the first processing includes AI processing, and / or, the second processing includes AI processing. In other words, the second data is the gradient data corresponding to the data obtained by performing AI processing on the first data, or the first data is the gradient data corresponding to the data obtained by performing AI processing on the second data. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to AI processing while also improving the flexibility of AI deployment.

[0302] In addition, the configuration information received by the first communication device in step S301 is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data. Accordingly, the first communication device can receive the first data based on the first information in step S302; and after the first communication device sends the second information for scheduling the transmission of the second data in step S303, the first communication device can send the second data based on the second information in step S304. In other words, in the case where the first data and / or the second data are data associated with AI processing, the first communication device can realize the transmission of the data associated with AI processing based on the scheduling of the first information and the second information. Thus, by scheduling the data associated with AI processing through the first information and the second information, the transmission success rate of the data associated with AI processing can be improved.

[0303] See also Figure 7 , the embodiment of the present application provides a communication device 700, which can implement the functions of the second communication device or the first communication device in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. In the embodiment of the present application, the communication device 700 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip.

[0304] It should be noted that the transceiver unit 702 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

[0305] In a possible implementation, when the device 700 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive configuration information, the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit 701 is used to receive the first data based on the first information; the transceiver unit 702 is also used to send second information, and the second information is used to schedule the transmission of the second data; the processing unit 701 is also used to send the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0306] In a possible implementation, when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to send configuration information, the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit 701 is used to send the first data based on the first information; the transceiver unit 702 is also used to receive the second information, and the second information is used to schedule the transmission of the second data; the processing unit 701 is also used to receive the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0307] It should be noted that the information execution process and other contents of the units of the above-mentioned communication device 700 can be specifically referred to the description in the method embodiment shown in the above-mentioned application, and will not be repeated here.

[0308] See also Figure 8 , is another schematic structural diagram of a communication device 800 provided in the present application, wherein the communication device 800 includes a logic circuit 801 and an input / output interface 802. The communication device 800 may be a chip or an integrated circuit.

[0309] in, Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Figure 8The input / output interface 802 in the communication interface may include an input interface and an output interface. Alternatively, the communication interface may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0310] Optionally, the input-output interface 802 is used to receive configuration information, which is used to configure transmission resources for first information, and the first information is used to schedule the transmission of first data; the logic circuit 801 is used to receive the first data based on the first information; the input-output interface 802 is also used to send second information, and the second information is used to schedule the transmission of second data; the logic circuit 801 is also used to send the second data based on the second information; wherein the first data is data after first processing and the second data is gradient data obtained based on the first data after second processing and label data, or the second data is data after first processing and the first data is gradient data obtained based on the second data after second processing and label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0311] Optionally, the input-output interface 802 is used to send configuration information, which is used to configure transmission resources of first information, and the first information is used to schedule the transmission of first data; the logic circuit 801 is used to send the first data based on the first information; the input-output interface 802 is also used to receive second information, and the second information is used to schedule the transmission of second data; the logic circuit 801 is also used to receive the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and label data of the first data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data of the second data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0312] The logic circuit 801 and the input / output interface 802 may also execute other steps executed by the first communication device or the second communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0313] In one possible implementation, Figure 7 The processing unit 701 shown can be Figure 8 The logic circuit 801 in.

[0314] Optionally, the logic circuit 801 may be a processing device, and the functions of the processing device may be partially or completely implemented by software. The functions of the processing device may be partially or completely implemented by software.

[0315] Optionally, the processing device may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and / or steps in any one of the method embodiments.

[0316] Alternatively, the processing device may include only a processor. A memory for storing a computer program is located outside the processing device, and the processor is connected to the memory via a circuit / wire to read and execute the computer program stored in the memory. The memory and the processor may be integrated together, or may be physically independent of each other.

[0317] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), system on chip (SoC), central processor unit (CPU), network processor (NP), digital signal processor (DSP), microcontroller unit (MCU), programmable logic device (PLD) or other integrated chips, or any combination of the above chips or processors.

[0318] See also Fig. 9 , is a communication device 900 involved in the above embodiment provided in an embodiment of the present application, and the communication device 900 may specifically be a communication device as a terminal device in the above embodiment, Fig. 9 The example shown is implemented by a terminal device (or a component in the terminal device).

[0319] Among them, a possible logical structure diagram of the communication device 900 is shown, and the communication device 900 may include but is not limited to at least one processor 901 and a communication port 902.

[0320] in, Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Fig. 9 The communication port 902 in the embodiment may include an input interface and an output interface. Alternatively, the communication port 902 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0321] Further optionally, the device may also include at least one of a memory 903 and a bus 904 . In an embodiment of the present application, the at least one processor 901 is used to control and process the actions of the communication device 900 .

[0322] In addition, the processor 901 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and the like. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0323] It should be noted that Fig. 9 The communication device 900 shown can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment, and achieve the corresponding technical effects of the terminal device. Fig. 9 The specific implementation methods of the communication device shown can all refer to the description in the aforementioned method embodiment, and will not be described in detail here.

[0324] See also Fig.10 , is a schematic diagram of the structure of the communication device 1000 involved in the above embodiment provided in the embodiment of the present application, and the communication device 1000 may specifically be the communication device as the network device in the above embodiment, Fig.10 The example shown is that the network device is implemented by the network device (or a component in the network device), wherein the structure of the communication device can refer to Fig.10 The structure shown.

[0325] The communication device 1000 includes at least one processor 1011 and at least one network interface 1014. Further optionally, the communication device also includes at least one memory 1012, at least one transceiver 1013 and one or more antennas 1015. The processor 1011, the memory 1012, the transceiver 1013 and the network interface 1014 are connected, for example, through a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 1015 is connected to the transceiver 1013. The network interface 1014 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1014 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.

[0326] in, Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Fig.10 The network interface 1014 in the embodiment may include an input interface and an output interface. Alternatively, the network interface 1014 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0327] The processor 1011 is mainly used to process the communication protocol and communication data, and to control the entire communication device, execute the software program, and process the data of the software program, for example, to support the communication device to perform the actions described in the embodiment. The communication device may include a baseband processor and a central processing unit. The baseband processor is mainly used to process the communication protocol and communication data, and the central processing unit is mainly used to control the entire terminal device, execute the software program, and process the data of the software program. Fig.10 The processor 1011 in the embodiment can integrate the functions of the baseband processor and the central processor. It can be understood by those skilled in the art that the baseband processor and the central processor can also be independent processors, which are interconnected through technologies such as buses. It can be understood by those skilled in the art that the terminal device can include multiple baseband processors to adapt to different network formats, and the terminal device can include multiple central processors to enhance its processing capabilities. The various components of the terminal device can be connected through various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The central processor can also be described as a central processing circuit or a central processing chip. The function of processing the communication protocol and the communication data can be built into the processor, or it can be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

[0328] The memory is mainly used to store software programs and data. The memory 1012 can be independent and connected to the processor 1011. Optionally, the memory 1012 can be integrated with the processor 1011, for example, integrated into a chip. Among them, the memory 1012 can store program codes for executing the technical solutions of the embodiments of the present application, and the execution is controlled by the processor 1011. The various types of computer program codes executed can also be regarded as drivers of the processor 1011.

[0329] Fig.10 Only one memory and one processor are shown. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device, etc. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the embodiments of the present application.

[0330] The transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal, and the transceiver 1013 can be connected to the antenna 1015. The transceiver 1013 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1015 can receive radio frequency signals, and the receiver Rx of the transceiver 1013 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or the digital intermediate frequency signal to the processor 1011, so that the processor 1011 further processes the digital baseband signal or the digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1013 is also used to receive a modulated digital baseband signal or a digital intermediate frequency signal from the processor 1011, and convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and send the radio frequency signal through one or more antennas 1015. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion processing on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal, and the order of the down-mixing and analog-to-digital conversion processing is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion processing on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal, and the order of the up-mixing and digital-to-analog conversion processing is adjustable. The digital baseband signal and the digital intermediate frequency signal can be collectively referred to as a digital signal.

[0331] The transceiver 1013 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver unit for implementing a receiving function may be regarded as a receiving unit, and a device in the transceiver unit for implementing a sending function may be regarded as a sending unit, that is, the transceiver unit includes a receiving unit and a sending unit, the receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the sending unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0332] It should be noted that Fig.10 The communication device 1000 shown can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and achieve the corresponding technical effects of the network device. Fig.10 The specific implementation methods of the communication device 1000 shown can all refer to the description in the aforementioned method embodiment, and will not be repeated here.

[0333] See also Fig.11 , which is a structural diagram of the communication device involved in the above-mentioned embodiments provided in the embodiments of the present application.

[0334] It can be understood that the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, etc., which are appropriately configured together to perform the technical solutions provided in the present application. The communication device 110 may be the terminal device or network device described above, or a component (such as a chip) in these devices, to implement the method described in the following method embodiment. The communication device 110 includes one or more processors 111. The processor 111 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control the communication device (such as a RAN node, a terminal, or a chip, etc.), execute software programs, and process data of software programs.

[0335] Optionally, in one design, the processor 111 may include a program 113 (sometimes also referred to as code or instruction), and the program 113 may be executed on the processor 111 to enable the communication device 110 to perform the method described in the following embodiments. In another possible design, the communication device 110 includes a circuit ( Fig.11 not shown).

[0336] Optionally, the communication device 110 may include one or more memories 112 on which a program 114 (sometimes also referred to as code or instructions) is stored. The program 114 can be executed on the processor 111 so that the communication device 110 executes the method described in the above method embodiment.

[0337] Optionally, the processor 111 and / or the memory 112 may include an AI module 117, 118, and the AI ​​module is used to implement AI-related functions. The AI ​​module may be implemented by software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a radio intelligence control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0338] Optionally, data may also be stored in the processor 111 and / or the memory 112. The processor and the memory may be provided separately or integrated together.

[0339] Optionally, the communication device 110 may further include a transceiver 115 and / or an antenna 116. The processor 111 may also be sometimes referred to as a processing unit, which controls the communication device (e.g., a RAN node or a terminal). The transceiver 115 may also be sometimes referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., which is used to implement the transceiver function of the communication device through the antenna 116.

[0340] in, Figure 7 The processing unit 701 shown may be the processor 111 . Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Fig.11 The transceiver 115 in the embodiment may include an input interface and an output interface. Alternatively, the transceiver 115 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0341] An embodiment of the present application further provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation methods of the first communication device or the second communication device in the aforementioned embodiment.

[0342] An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method that may be implemented by the above-mentioned first communication device or second communication device.

[0343] An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data for the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, and may also include chips and other discrete devices, wherein the communication device can specifically be the first communication device or the second communication device in the aforementioned method embodiment.

[0344] An embodiment of the present application also provides a communication system, and the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0345] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0346] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0347] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

Claims

1. A communication method, characterized in that: include: receiving configuration information, where the configuration information is used to configure transmission resources for first information, where the first information is used to schedule transmission of first data; receiving the first data based on the first information; Sending second information, where the second information is used to schedule transmission of second data; sending the second data based on the second information; Among them, the first data is data after a first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or the second data is data after a first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes artificial intelligence AI processing, and / or the second processing includes AI processing.

2. The method according to claim 1, characterized in that: The transmission resource of the first information includes a time domain resource that carries the first information; The time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

3. The method according to claim 1, characterized in that The first data is data after a first processing, and the second data is gradient data obtained based on the data after a second processing of the first data and label data; The first process satisfies any one of the following: the first process is triggered based on the second information, and the first information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

4. The method according to claim 1 or 3, characterized in that: The second data is data after the first processing, and the first data is gradient data obtained based on the data after the second processing and label data of the second data; The first process satisfies any one of the following: the first process is triggered based on the first information, and the second information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

5. The method according to claim 3 or 4, characterized in that: The configuration information includes a first configuration and a second configuration, the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information; The time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Sending first indication information, where the first indication information is used to indicate whether the first information is received correctly.

7. The method according to claim 6, characterized in that The first indication information indicates that the first information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first indication information is also used to trigger the first processing; The first indication information indicates that the first information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the first indication information is also used to trigger the second processing.

8. The method according to claim 6 or 7, characterized in that: The first indication information is also used to indicate whether to perform processing based on the first data.

9. The method according to any one of claims 1 to 8, characterized in that: After sending the second information, the method further includes: Second indication information is received, where the second indication information is used to indicate whether the second information is received correctly.

10. The method according to claim 9, characterized in that The second indication information indicates that the second information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the second indication information is also used to trigger the second processing; The second indication information indicates that the second information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second indication information is used to trigger the first processing.

11. The method according to claim 9 or 10, characterized in that: The second indication information is also used to indicate whether to perform processing based on the second data.

12. The method according to any one of claims 1 to 11, characterized in that: The second information is further used to indicate at least one of the following: The data type of the second data, and whether to send gradient information determined based on the second data.

13. The method according to any one of claims 1 to 12, characterized in that: The first information is further used to indicate at least one of the following: The data type of the first data, and whether to send gradient information determined based on the first data.

14. The method according to any one of claims 1 to 13, characterized in that: The method further comprises: Sending capability information of a first communication device, where the capability information of the first communication device is used to determine the configuration information; The AI ​​capability information of the first communication device includes at least one of the following: The processing delay information of the first communication device on the forward data of the AI ​​network structure to which the first data belongs, the processing delay information of the first communication device on the reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, the load information of the processing resources of the first communication device, and the computing resource information of the first communication device.

15. The method according to any one of claims 1 to 14, characterized in that The configuration information includes at least one of the following: The period of the first information, the window length for detecting the first information, and the position of the transmission symbol of the first information in the time slot.

16. The method according to any one of claims 1 to 15, characterized in that The method further comprises: Receive third information, where the third information is used to indicate at least one of the following: Information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

17. A communication method, characterized in that: include: Sending configuration information, where the configuration information is used to configure transmission resources for first information, where the first information is used to schedule transmission of first data; sending the first data based on the first information; receiving second information, where the second information is used to schedule transmission of second data; receiving the second data based on the second information; Among them, the first data is data after a first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or the second data is data after a first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes artificial intelligence AI processing, and / or the second processing includes AI processing.

18. The method according to claim 17, characterized in that The transmission resource of the first information includes a time domain resource that carries the first information; The time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

19. The method according to claim 17, characterized in that The first data is data after a first processing, and the second data is gradient data obtained based on the data after a second processing of the first data and label data; The first process satisfies any one of the following: the first process is triggered based on the second information, and the first information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

20. The method according to claim 17 or 19, characterized in that The second data is data after the first processing, and the first data is gradient data obtained based on the data after the second processing and label data of the second data; The first process satisfies any one of the following: the first process is triggered based on the first information, and the second information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

21. The method according to claim 19 or 20, characterized in that The configuration information includes a first configuration and a second configuration, the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information; The time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

22. The method according to any one of claims 17 to 21, characterized in that The method further comprises: First indication information is received, where the first indication information is used to indicate whether the first information is received correctly.

23. The method according to claim 22, characterized in that The first indication information indicates that the first information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first indication information is also used to trigger the first processing; The first indication information indicates that the first information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the first indication information is also used to trigger the second processing.

24. The method according to claim 22 or 23, characterized in that The first indication information is also used to indicate whether to perform processing based on the first data.

25. The method according to any one of claims 17 to 24, characterized in that After receiving the second information, the method further includes: Send second indication information, where the second indication information is used to indicate whether the second information is received correctly.

26. The method according to claim 25, characterized in that The second indication information indicates that the second information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the second indication information is also used to trigger the second processing; The second indication information indicates that the second information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second indication information is used to trigger the first processing.

27. The method according to claim 25 or 26, characterized in that The second indication information is also used to indicate whether to perform processing based on the second data.

28. The method according to any one of claims 17 to 27, characterized in that The second information is further used to indicate at least one of the following: The data type of the second data, and whether to send gradient information determined based on the second data.

29. The method according to any one of claims 17 to 28, characterized in that The first information is further used to indicate at least one of the following: The data type of the first data, and whether to send gradient information determined based on the first data.

30. The method according to any one of claims 17 to 29, characterized in that The method further comprises: receiving capability information of a first communication device, wherein the capability information of the first communication device is used to determine the configuration information; The AI ​​capability information of the first communication device includes at least one of the following: The processing delay information of the first communication device on the forward data of the AI ​​network structure to which the first data belongs, the processing delay information of the first communication device on the reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, the load information of the processing resources of the first communication device, and the computing resource information of the first communication device.

31. The method according to any one of claims 17 to 30, characterized in that The configuration information includes at least one of the following: The period of the first information, the window length for detecting the first information, and the position of the transmission symbol of the first information in the time slot.

32. The method according to any one of claims 17 to 31, characterized in that The method further comprises: Sending third information, where the third information is used to indicate at least one of the following: Information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

33. A communication device, characterized in that: Comprising means for performing the method as claimed in any one of claims 1 to 32.

34. A communication device, characterized in that: The method comprises at least one processor coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 32.

35. The communication device according to claim 34, characterized in that The communication device is a chip or a chip system.

36. A readable storage medium, characterized in that: The storage medium stores a computer program or an instruction, and when the computer program or the instruction is executed by the communication device, the method according to any one of claims 1 to 32 is implemented.

37. A computer program product, characterized in that The method comprises instructions which, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 32.

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